研究细胞力学如何影响细胞功能和疾病进展需要细胞力学特性成像技术。机械显微镜（压缩光学相干弹性成像的高分辨率变体）根据光学相干显微镜 (OCM) B 扫描之间的相位差生成受压缩样品的弹性图像。然而，现有的机械显微镜信号处理链（称为代数方法）假设样品应力是单轴且轴向均匀的，违反这些假设会降低弹性图像的准确性和精度。此外，它没有考虑有关样品几何形状或机械性能分布的先验信息。在这项研究中，我们研究了训练条件生成对抗网络 (cGAN) 以从包含嵌入水凝胶中的细胞球体的样本的相位差图像生成弹性图像的可行性。为了构建 cGAN 训练和模拟测试集，我们生成了 30,000 个样本使用参数模型生成人造弹性图像，并使用有限元分析计算相应的相位差图像，以模拟应用于人造样品的压缩。我们还使用机械显微镜对嵌入水凝胶中的真实 MCF7 乳腺肿瘤球体进行成像，以构建实验测试集，并使用代数弹性图像以及共同配准的 OCM 和共焦荧光显微镜 (CFM) 图像评估 cGAN。与模拟测试集进行比较地面实况弹性图像显示，cGAN 产生的均方根误差（中位数：3.47 kPa，95% 置信区间 (CI) [3.41, 3.52]）比代数方法（中位数：4.91 kPa，95% CI [4.85, 4.97]）更低]）。对于实验测试集，cGAN 弹性图像在与代数弹性、OCM 和 CFM 图像中看到的核相对应的位置处包含类似于僵硬核的特征。此外，cGAN 弹性图像比代数弹性图像具有更高的分辨率，并且对噪声具有更强的鲁棒性。对于模拟和真实实验数据，cGAN 弹性图像都比代数弹性图像表现出更好的准确性、空间分辨率、灵敏度和对噪声的鲁棒性。版权所有 © 2024。由 Elsevier B.V. 出版

Techniques for imaging the mechanical properties of cells are needed to study how cell mechanics influence cell function and disease progression. Mechano-microscopy (a high-resolution variant of compression optical coherence elastography) generates elasticity images of a sample undergoing compression from the phase difference between optical coherence microscopy (OCM) B-scans. However, the existing mechano-microscopy signal processing chain (referred to as the algebraic method) assumes the sample stress is uniaxial and axially uniform, such that violation of these assumptions reduces the accuracy and precision of elasticity images. Furthermore, it does not account for prior information regarding the sample geometry or mechanical property distribution. In this study, we investigate the feasibility of training a conditional generative adversarial network (cGAN) to generate elasticity images from phase difference images of samples containing a cell spheroid embedded in a hydrogel.To construct the cGAN training and simulated test sets, we generated 30,000 artificial elasticity images using a parametric model and computed the corresponding phase difference images using finite element analysis to simulate compression applied to the artificial samples. We also imaged real MCF7 breast tumor spheroids embedded in hydrogel using mechano-microscopy to construct the experimental test set and evaluated the cGAN using the algebraic elasticity images and co-registered OCM and confocal fluorescence microscopy (CFM) images.Comparison with the simulated test set ground truth elasticity images shows the cGAN produces a lower root mean square error (median: 3.47 kPa, 95 % confidence interval (CI) [3.41, 3.52]) than the algebraic method (median: 4.91 kPa, 95 % CI [4.85, 4.97]). For the experimental test set, the cGAN elasticity images contain features resembling stiff nuclei at locations corresponding to nuclei seen in the algebraic elasticity, OCM, and CFM images. Furthermore, the cGAN elasticity images are higher resolution and more robust to noise than the algebraic elasticity images.The cGAN elasticity images exhibit better accuracy, spatial resolution, sensitivity, and robustness to noise than the algebraic elasticity images for both simulated and real experimental data.Copyright © 2024. Published by Elsevier B.V.